Programme And Module Handbook
 
Course Details in 2020/21 Session


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Module Title LM Nonlinear Programming I and Heuristic Optimisation
SchoolMathematics
Department Mathematics
Module Code 06 33862
Module Lead Prof Michal Kocvara
Level Masters Level
Credits 20
Semester Semester 1
Pre-requisites LI Linear Algebra & Linear Programming - (06 25765)
Co-requisites
Restrictions None
Contact Hours Guided independent study-144 hours
Tutorial-10 hours
Lecture-46 hours
Total: 200 hours
Exclusions
Description Many decision problems arising in managerial decision making in the public as well as in the private sector are inherently nonlinear, and the same holds for various problems occurring in science and engineering. Tackling highly realistic nonlinear problems leads to solution methods totally different from those of, say, linear programming. In this course, the essential ideas as well as some of the most important solution algorithms for nonlinear decision problems are studied.

Most problems from management mathematics (discrete or continuous) are NP-hard. In other words, optimisation problems that arise in industry or in the public sector could not be solved exactly in reasonable computing time, even with modern computers. Therefore, when traditional mathematics techniques fail to give a fast answer, one should rely on near-optimal solution methods or heuristics. Ideas of classical heuristics (local search, hill climbing, greedy search, divide and conquer, A* search, dynamic programming etc.) will be studied first. A modern heuristics (metaheuristics) or general frameworks for building heuristics, usually gives rules of escaping from the so-called "local optima trap". Such methods are Tabu search, Simulated Annealing, Evolutionary Algorithms, Genetic Algorithms etc.
Learning Outcomes By the end of the module students should be able to:
  • Understand and explain the basic concepts of nonlinear programming
  • Understand and explain first and second order optimality conditions
  • Understand and explain the role of convexity in optimisation
  • Explore this topic beyond the taught syllabus
  • Understand and explain why and when heuristic optimisation techniques are useful in Management Mathematics
  • Understand and explain the basic concepts of classical heuristic optimisation techniques
  • Design data structure for the computer code and apply rules of heuristics for that problem
  • Explore this topic beyond the taught syllabus
Assessment 33862-01 : Raw Module Mark : Coursework (100%)
Assessment Methods & Exceptions Assessment: Online January Assessment (50%); In-course Assessment (50%).
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